The present disclosure relates generally to the field of spectroscopy and, more particularly, to a system and method of processing spectroscopic data.
This section is intended to introduce the reader to various aspects of art that may be related to various aspects of the present disclosure, which are described and/or claimed below. This discussion is believed to be helpful in providing the reader with background information to facilitate a better understanding of the various aspects of the present disclosure. Accordingly, it should be understood that these statements are to be read in this light, and not as admissions of prior art.
Spectroscopy may be employed to ascertain the existence and/or concentration of component chemicals in a sample. To perform a spectroscopic analysis on a sample, a source may first send electromagnetic radiation through the sample. The spectrum of electromagnetic radiation which passes through the sample may indicate the absorbance and/or scattering of various constituent components of the sample. Based on the amount and spectrum of the sample absorbance, the presence and/or concentration of distinct chemicals may be detected by employing methods of spectrographic data processing.
Common spectrographic data processing techniques for multicomponent samples may include, for example, regression analysis involving ordinary least squares (OLS) or partial least squares (PLS). Such techniques may rely on assumptions that the signal strength of each component's absorbance spectrum is proportional to its concentration and that the component spectra add linearly.
The above assumptions do not hold, however, for all multicomponent samples. When electromagnetic radiation passes through a turbid multicomponent sample, for example, the radiation is scattered and the radiation path changes. As a result, radiation exiting the sample may not be detected or, if detected, the absorbance spectra of the radiation may change as the radiation takes a more circuitous path through the sample. Radiation passing through a heterogeneous multicomponent sample may produce similarly distorted results, as some of the radiation may be neither scattered nor absorbed by the sample. As a result, the perceived effective absorption of the sample has a nonlinear response to changes in absorption coefficient. Real-world examples of turbid and heterogeneous multicomponent samples may include, for example, whole blood or mixtures from industrial chemical processes.
Optional preprocessing, such as the software package EMSC, may correct for some deficiencies in the Beer's Law linear model which may result with some multicomponent samples. However, the techniques discussed above may not necessarily produce positive concentrations and may fail to account for the presence of unknown components. Moreover, the techniques may not admit a formulation suitable for heteroscedastic models, may not allow simultaneous projection of spectra onto row and column spaces, and/or may be exceedingly complex.